Alumni
We thank all of our project alumni who are no longer with us.
Project Leaflet
Leaflet Health is a digital health startup that offers a digital platform for clinicians to seamlessly send health information to patients, with a focus on those from culturally-and-linguistically diverse backgrounds. Through a web and mobile application, patients receive important, clinician-curated health information, such as post-operative care instructions.
In addition, Leaflet Health enables clinicians to collect patient-reported outcome measures (PROMs) via guided questionnaires. This data helps healthcare providers monitor patient progress and adjust treatments as needed, improving the continuity and efficacy of care.
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Developed in partnership with Western Health, Leaflet Health has been successfully integrated across various surgical, medical, and outpatient settings, demonstrating its value in an integrated, patient-centred model of healthcare delivery.
Project miRmaids
Project miRmaids aims to design a "biological logic circuit" using synthetic biology, computational biology, and bioinformatics methods. In short, Project miRmaids will engineer cell lines where the expression of certain proteins is controlled by microRNAs (miRs). Such a system may offer quick and reversible ways of modifying cells' phenotypes and activity.
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Given the need for both wet- and dry-lab expertise, team members include biomedical, medical, mathematics, and computer science students.
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​Project miRmaids has secured the support of Co-Labs and the University of Melbourne. It aims to present its work at the Australasian Synthetic Biology Challenge and the international iGEM competition.
Austin Radiology Project
The Austin Radiology Project comprises two sub-Projects, both of which work in partnership with the Radiology Department at Austin Health. They offer a unique opportunity for students to work with AI technology and real world radiological data on clinically important questions.
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The first sub-Project utilised machine-learning methods to predict optimal inversion times on cardiac magnetic resonance imaging, resulting in a pending academic publication. The second sub-Project will investigate the utility of AI in interpreting radiological data in the context of osteoporosis.
Members of the Austin Radiology Project include junior doctors, medical, engineering, and computer science students.
